Predicting cryptocurrency defaults - Grobys, Klaus; Sapkota, Niranjan (Taylor & Francis, 2020)
We examine all available 146 Proof-of-Work-based cryptocurrencies that started trading prior to the end of 2014 and track their performance until December 2018. We find that about 60% of those cryptocurrencies were eventually in default. The substantial sums of money involved mean those bankruptcies will have an enormous societal impact. Employing cryptocurrency-specific data, we estimate a model based on linear discriminant analysis to predict such defaults. Our model is capable of explaining 87% of cryptocurrency bankruptcies after only one month of trading and could serve as a screening tool for investors keen to boost overall portfolio performance and avoid investing in unreliable cryptocurrencies.
Keywords: Cryptocurrency; bitcoin; bankruptcy; default; credit risk; fi=Laskentatoimi ja rahoitus|en=Accounting and Finance|